1
00:00:15,356 --> 00:00:22,876
Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the podcast

2
00:00:22,876 --> 00:00:26,036
Speaker 1: where we explore the stories behind the stories in the news.

3
00:00:26,556 --> 00:00:30,076
Speaker 1: I'm Noah Feldman. If you've been listening to our show

4
00:00:30,276 --> 00:00:34,156
Speaker 1: this past month, you'll know that coronavirus is basically the

5
00:00:34,276 --> 00:00:38,156
Speaker 1: only thing we've been thinking about. We've tried to explore

6
00:00:38,316 --> 00:00:42,156
Speaker 1: how this global pandemic is influencing every aspect of our lives.

7
00:00:42,676 --> 00:00:46,676
Speaker 1: Our body is, of course, our economy, our civil liberties,

8
00:00:47,276 --> 00:00:51,676
Speaker 1: our emotions, even how we cook. One topic, though, that

9
00:00:51,716 --> 00:00:54,636
Speaker 1: we haven't yet covered is the question of treatment that

10
00:00:54,676 --> 00:00:58,036
Speaker 1: takes on the virus directly. That may be one way

11
00:00:58,076 --> 00:01:01,076
Speaker 1: for us to solve the corona problem, although it also

12
00:01:01,196 --> 00:01:03,516
Speaker 1: may not be depending on the science. We may simply

13
00:01:03,556 --> 00:01:06,676
Speaker 1: have to depend ultimately on social isolation to make the

14
00:01:06,756 --> 00:01:10,076
Speaker 1: virus go down. So how far are we along the

15
00:01:10,116 --> 00:01:13,756
Speaker 1: way to determining what drugs work? Here? Are the treatments

16
00:01:13,756 --> 00:01:16,676
Speaker 1: that have gotten a lot of publicity actually working? Are

17
00:01:16,716 --> 00:01:20,436
Speaker 1: they plausible? Have the studies that have been released actually

18
00:01:20,476 --> 00:01:23,396
Speaker 1: demonstrated efficacy? Or are we much too early in the

19
00:01:23,476 --> 00:01:25,516
Speaker 1: process to know how things are going to work out?

20
00:01:26,236 --> 00:01:29,196
Speaker 1: If we find a treatment, will it be scalable? Here

21
00:01:29,196 --> 00:01:31,796
Speaker 1: to discuss all of this with me is doctor Angela

22
00:01:31,876 --> 00:01:35,596
Speaker 1: russ Mussel. She's a research scientist and a virologist at

23
00:01:35,636 --> 00:01:39,556
Speaker 1: the Columbia University Mailman School of Public Health, and she's

24
00:01:39,556 --> 00:01:47,556
Speaker 1: worked extensively on viruses and evola. Angela, first of all,

25
00:01:47,596 --> 00:01:50,556
Speaker 1: thank you for taking the time to be here with us.

26
00:01:51,516 --> 00:01:55,916
Speaker 1: What treatments are out there now that are being studied

27
00:01:56,516 --> 00:01:59,796
Speaker 1: that you think potentially holds some promise And we're not

28
00:01:59,836 --> 00:02:02,236
Speaker 1: talking about vaccines at all today, but we're going to

29
00:02:02,316 --> 00:02:06,476
Speaker 1: start by talking just about treatments, right So, there are

30
00:02:06,556 --> 00:02:10,756
Speaker 1: several treatments in clinical trial right now. It's important to

31
00:02:10,836 --> 00:02:13,516
Speaker 1: note that there are no treatments at this time that

32
00:02:13,556 --> 00:02:18,196
Speaker 1: have been demonstrated to be effective and safe for treating

33
00:02:18,236 --> 00:02:22,876
Speaker 1: COVID nineteen. The main drugs that I've been hearing about

34
00:02:22,916 --> 00:02:27,036
Speaker 1: are three different drug regiments. So there is a drug

35
00:02:27,036 --> 00:02:31,396
Speaker 1: called rumdz a viere that was tested for ebola, and

36
00:02:31,476 --> 00:02:35,116
Speaker 1: it wasn't effective for ebola, but it's a fairly broad

37
00:02:35,196 --> 00:02:38,676
Speaker 1: spectrum anti viral drug and it has shown promise against

38
00:02:39,036 --> 00:02:44,876
Speaker 1: MERS coronavirus in animals and against this coronavirus SARS coronavirus

39
00:02:44,916 --> 00:02:49,996
Speaker 1: two in cell culture experiments. So that drug is being tested.

40
00:02:50,516 --> 00:02:55,356
Speaker 1: It's not currently approved for use in humans, but there

41
00:02:55,516 --> 00:02:59,116
Speaker 1: is quite a bit of safety data that was gathered

42
00:02:59,196 --> 00:03:03,156
Speaker 1: during the trials for ebola, So if that drug does

43
00:03:03,276 --> 00:03:06,316
Speaker 1: prove to be effective in a controlled trial, it should

44
00:03:06,316 --> 00:03:11,636
Speaker 1: be relatively quick process to get it out to the public.

45
00:03:12,116 --> 00:03:15,596
Speaker 1: Another drug that's being looked at is hydroxy chloroquin, which

46
00:03:15,636 --> 00:03:18,836
Speaker 1: is an anti malarial drug that's also used to treat

47
00:03:18,916 --> 00:03:24,276
Speaker 1: rheumatoid arthritis and lupus. Sometimes that has been tested with

48
00:03:24,556 --> 00:03:28,916
Speaker 1: the antibiotic zithromycin. There was a lot of press about

49
00:03:28,956 --> 00:03:33,276
Speaker 1: this also because President Trump has asserted that this is

50
00:03:33,276 --> 00:03:37,036
Speaker 1: a game changing, miracle drug. It's really important to note

51
00:03:37,116 --> 00:03:40,596
Speaker 1: that the controlled studies in a sufficient number of patients

52
00:03:40,636 --> 00:03:43,876
Speaker 1: to determine whether or not it's effective are still ongoing.

53
00:03:44,396 --> 00:03:47,316
Speaker 1: So the only papers that have been out about chloroquin

54
00:03:47,636 --> 00:03:51,316
Speaker 1: or hydroxy chloroquin have used very very small numbers of

55
00:03:51,356 --> 00:03:54,916
Speaker 1: patients and there were some issues with the trial design

56
00:03:55,036 --> 00:03:58,996
Speaker 1: in terms of the controls that were used to evaluate

57
00:03:59,036 --> 00:04:03,956
Speaker 1: its efficacy. So despite what we've heard some politicians talking about,

58
00:04:04,436 --> 00:04:08,316
Speaker 1: those drugs are not proven at this time to be

59
00:04:08,396 --> 00:04:12,916
Speaker 1: effective at treating COVID and then Finally, they're also looking

60
00:04:12,996 --> 00:04:17,356
Speaker 1: at a combination of HIV protease inhibitors, and these drugs

61
00:04:17,396 --> 00:04:23,716
Speaker 1: were chosen because they showed some efficacy anecdotally against SARS

62
00:04:23,796 --> 00:04:28,356
Speaker 1: classic and so people thought that they might be useful here. Additionally,

63
00:04:28,396 --> 00:04:32,396
Speaker 1: they were used with an anti influenza drug to treat

64
00:04:32,436 --> 00:04:37,156
Speaker 1: a patient in Thailand. That patient recovered, so they concluded

65
00:04:37,196 --> 00:04:40,236
Speaker 1: that it might have had an effect. But as with

66
00:04:40,436 --> 00:04:43,796
Speaker 1: the other drugs that I just mentioned, it's critical to

67
00:04:43,836 --> 00:04:47,076
Speaker 1: test those in a large group of patients with proper

68
00:04:47,116 --> 00:04:50,796
Speaker 1: controls to determine if they actually are effective or not.

69
00:04:51,436 --> 00:04:55,356
Speaker 1: A smaller clinical trial done in China that was controlled

70
00:04:55,396 --> 00:04:59,596
Speaker 1: and randomized looked at those two HIV drugs and saw

71
00:04:59,716 --> 00:05:03,836
Speaker 1: no effect in overall outcome, meaning that whether a patient

72
00:05:03,916 --> 00:05:07,916
Speaker 1: received those drugs or just supportive care, there was no

73
00:05:07,996 --> 00:05:11,796
Speaker 1: difference in the number of patients that eventually died from

74
00:05:11,876 --> 00:05:16,956
Speaker 1: severe COVID disease. So the WHO is evaluating that drug

75
00:05:16,996 --> 00:05:19,556
Speaker 1: combination as well in a larger group of patients to

76
00:05:19,636 --> 00:05:23,196
Speaker 1: see if at a population level that would have a

77
00:05:23,276 --> 00:05:27,636
Speaker 1: more a greater impact in terms of treating COVID. So

78
00:05:27,676 --> 00:05:30,516
Speaker 1: those are the three drug regimens that have advanced the

79
00:05:30,556 --> 00:05:34,516
Speaker 1: furthest in clinical trials, and we should start seeing some

80
00:05:34,596 --> 00:05:37,236
Speaker 1: trial data from those, I would think in the next

81
00:05:37,276 --> 00:05:40,396
Speaker 1: couple months, so that we can have a better idea

82
00:05:40,556 --> 00:05:43,316
Speaker 1: of whether or not some of these existing drugs can

83
00:05:43,356 --> 00:05:48,636
Speaker 1: be repurposed for treating COVID. Why is the timescale months

84
00:05:49,276 --> 00:05:52,556
Speaker 1: for clinical trials here? I mean, the course of the

85
00:05:52,596 --> 00:05:57,556
Speaker 1: disease is not that long. And of course, ordinarily one

86
00:05:57,596 --> 00:06:00,636
Speaker 1: would want to have very careful experimental design and when

87
00:06:00,636 --> 00:06:03,196
Speaker 1: we would want to have peer review of the studies

88
00:06:03,236 --> 00:06:05,916
Speaker 1: and so forth and so on. But under crisis conditions,

89
00:06:05,916 --> 00:06:08,916
Speaker 1: the ordinary person, and I'm treating myself as that here, think,

90
00:06:09,676 --> 00:06:12,396
Speaker 1: you know, why can't the duration of the study just

91
00:06:12,556 --> 00:06:15,116
Speaker 1: be as long as the course of the disease runs.

92
00:06:15,116 --> 00:06:17,076
Speaker 1: And even if you run it a couple of times,

93
00:06:17,116 --> 00:06:19,276
Speaker 1: that doesn't mean that it has to take months. So

94
00:06:19,396 --> 00:06:22,596
Speaker 1: why are we talking about months? There are several factors.

95
00:06:22,956 --> 00:06:26,316
Speaker 1: In order to demonstrate efficacy, you really need to have

96
00:06:26,516 --> 00:06:31,156
Speaker 1: larger patient groups. So people in different places are doing

97
00:06:31,196 --> 00:06:34,956
Speaker 1: different things, they are in different environments, and they have

98
00:06:35,036 --> 00:06:39,276
Speaker 1: different levels of hospital care. And also genetically we're very

99
00:06:39,316 --> 00:06:42,396
Speaker 1: different from one another. There's a lot of individuals, individual

100
00:06:42,476 --> 00:06:46,156
Speaker 1: variation in the population. So when you are trying to

101
00:06:46,276 --> 00:06:49,556
Speaker 1: enroll patients in a clinical trial, you're going to be

102
00:06:49,596 --> 00:06:53,036
Speaker 1: seeing people coming from all different sorts of circumstances with

103
00:06:53,116 --> 00:06:58,516
Speaker 1: all different kinds of potentially confounding variables, so pre existing

104
00:06:58,556 --> 00:07:01,716
Speaker 1: medical conditions. People are going to be different ages, They're

105
00:07:01,756 --> 00:07:04,796
Speaker 1: going to be both male and female. Each case will

106
00:07:04,836 --> 00:07:08,756
Speaker 1: be different. So in order to understand how these drugs

107
00:07:08,796 --> 00:07:11,996
Speaker 1: work in general for a person off the street, you

108
00:07:12,036 --> 00:07:15,236
Speaker 1: really need to look at a lot of people, and

109
00:07:15,316 --> 00:07:18,436
Speaker 1: that just takes a lot of time. Another issue is

110
00:07:18,516 --> 00:07:22,276
Speaker 1: the ethics of it. So when clinical trials are done,

111
00:07:22,676 --> 00:07:25,876
Speaker 1: you need to have informed consent from all of those patients,

112
00:07:26,396 --> 00:07:28,676
Speaker 1: and patients are allowed to drop out of the trial

113
00:07:28,796 --> 00:07:32,916
Speaker 1: at any time. In addition, many clinical trials will have

114
00:07:33,636 --> 00:07:36,916
Speaker 1: criteria for patients to be removed from the trial if

115
00:07:36,956 --> 00:07:40,556
Speaker 1: it appears that they are being harmed by the trial itself.

116
00:07:41,076 --> 00:07:44,876
Speaker 1: Let's say somebody has an allergic reaction to an experimental drug.

117
00:07:45,476 --> 00:07:48,316
Speaker 1: You would not want to continue treating that patient with

118
00:07:48,356 --> 00:07:52,316
Speaker 1: that drug because that could potentially harm them. So in

119
00:07:52,436 --> 00:07:55,156
Speaker 1: order to get these types of numbers that we really

120
00:07:55,196 --> 00:07:58,836
Speaker 1: need to apply the knowledge of whether a drug is

121
00:07:58,876 --> 00:08:03,716
Speaker 1: effective or not and safe to a large population of people.

122
00:08:03,876 --> 00:08:06,036
Speaker 1: You just really need to enroll a lot of patients,

123
00:08:06,076 --> 00:08:08,236
Speaker 1: and they have to be able to remain in the trial,

124
00:08:09,116 --> 00:08:11,156
Speaker 1: and you have to do quite a lot of statistical

125
00:08:11,196 --> 00:08:14,316
Speaker 1: analysis to make sure that you're accounting for all of

126
00:08:14,356 --> 00:08:18,876
Speaker 1: these potential variables that a large diverse population of people bring.

127
00:08:19,876 --> 00:08:24,756
Speaker 1: Everything you just said seems completely logical and appropriate for

128
00:08:24,996 --> 00:08:28,476
Speaker 1: a well designed clinical trial of a drug. It makes

129
00:08:28,476 --> 00:08:32,076
Speaker 1: me very happy and relieved to think that under ordinary circumstances,

130
00:08:32,476 --> 00:08:35,396
Speaker 1: when scientists start checking on the efficacy of a new

131
00:08:35,476 --> 00:08:38,996
Speaker 1: potential treatment, they do everything you've said, large sample size,

132
00:08:39,236 --> 00:08:43,236
Speaker 1: sophisticated statistical analysis, ethical constraints, and allowing people to withdraw.

133
00:08:43,556 --> 00:08:46,636
Speaker 1: All that makes perfect sense. None of that makes sense

134
00:08:46,636 --> 00:08:49,276
Speaker 1: to me under crisis conditions. And help me out here,

135
00:08:49,276 --> 00:08:52,876
Speaker 1: because my instincts are clearly at odds with that of

136
00:08:53,356 --> 00:08:56,196
Speaker 1: at least some of the scientific community here. But it

137
00:08:56,276 --> 00:08:59,036
Speaker 1: just seems very difficult for me to get my head

138
00:08:59,036 --> 00:09:02,636
Speaker 1: around the idea that we should proceed as normal when

139
00:09:03,036 --> 00:09:07,436
Speaker 1: we're in the middle of a global pandemic. Certainly this

140
00:09:07,516 --> 00:09:11,676
Speaker 1: has been accelerated. The process of approving the trial, of

141
00:09:11,756 --> 00:09:15,556
Speaker 1: coordinating a trial has been accelerated. Another thing that is

142
00:09:15,636 --> 00:09:19,076
Speaker 1: being done sort of to balance potential benefit of these

143
00:09:19,196 --> 00:09:21,516
Speaker 1: drugs for patients who need it most, who don't have

144
00:09:21,596 --> 00:09:24,236
Speaker 1: time to wait for a clinical trial, because as you

145
00:09:24,316 --> 00:09:27,716
Speaker 1: pointed out, it is a public health crisis. The FDA

146
00:09:27,836 --> 00:09:33,316
Speaker 1: has approved the use of hydroxychloroquin prescriptions off label for

147
00:09:33,436 --> 00:09:38,356
Speaker 1: compassionate use in patients that really have nothing left to lose,

148
00:09:38,436 --> 00:09:41,476
Speaker 1: that patients who will die without some kind of intervention.

149
00:09:42,116 --> 00:09:47,516
Speaker 1: So there is a balance between doing these types of randomized,

150
00:09:47,796 --> 00:09:52,876
Speaker 1: properly controlled, properly statistically powered clinical trials as well as

151
00:09:52,956 --> 00:09:56,196
Speaker 1: access to the drugs for patients who really have nothing

152
00:09:56,276 --> 00:09:59,916
Speaker 1: left to lose, and well, even if they don't benefit,

153
00:10:00,316 --> 00:10:04,636
Speaker 1: it's worth a try. The danger in that is that

154
00:10:04,716 --> 00:10:09,996
Speaker 1: when people are deciding to self medicate or demanding these

155
00:10:10,036 --> 00:10:14,716
Speaker 1: prescriptions off label for disease that's potentially not very severe,

156
00:10:15,196 --> 00:10:18,276
Speaker 1: that's the type of thing that should be avoided. But

157
00:10:18,796 --> 00:10:21,436
Speaker 1: you are correct that it is a crisis and that

158
00:10:21,556 --> 00:10:25,596
Speaker 1: patients who are in the most dire condition, with the

159
00:10:25,596 --> 00:10:28,476
Speaker 1: most severe need should have access to some of these

160
00:10:28,516 --> 00:10:32,076
Speaker 1: experimental medications. Whether we have proof that they work or not.

161
00:10:33,036 --> 00:10:44,956
Speaker 1: We'll be back in just a moment. I now want

162
00:10:44,996 --> 00:10:49,716
Speaker 1: to ask you about the French study which I myself read,

163
00:10:50,116 --> 00:10:52,716
Speaker 1: where on a very very small sample size there were

164
00:10:52,716 --> 00:10:54,876
Speaker 1: fewer than forty five people in the entire trial, and

165
00:10:54,916 --> 00:10:56,836
Speaker 1: of those only a very very small number I think,

166
00:10:56,836 --> 00:11:00,716
Speaker 1: only six if I remember correctly, got the particular combination

167
00:11:01,156 --> 00:11:07,516
Speaker 1: of hydroxy chloroquine and zethromycin together. But that study was exciting,

168
00:11:07,556 --> 00:11:09,716
Speaker 1: and you could sort of understand why there was such

169
00:11:09,756 --> 00:11:13,516
Speaker 1: a reaction to it, even from the President, by virtue

170
00:11:13,556 --> 00:11:15,596
Speaker 1: of the fact that among that tiny number of people,

171
00:11:15,636 --> 00:11:20,036
Speaker 1: those six people, the study reported that all had been

172
00:11:20,036 --> 00:11:24,236
Speaker 1: completely cleared of signs of the virus when they study

173
00:11:24,276 --> 00:11:26,916
Speaker 1: them quite a short time later, something like six days later.

174
00:11:26,956 --> 00:11:29,236
Speaker 1: I mean, reading that study shows you what got everybody excited,

175
00:11:29,276 --> 00:11:31,956
Speaker 1: also shows you the limitations of the study. What do

176
00:11:31,996 --> 00:11:34,636
Speaker 1: you think of as the real problem with that study?

177
00:11:34,636 --> 00:11:38,156
Speaker 1: If there is one? Oh boy, there are several problems

178
00:11:38,156 --> 00:11:41,276
Speaker 1: with that study. One of the issues were the controls

179
00:11:41,316 --> 00:11:44,876
Speaker 1: that were selected for that study. They were patients from

180
00:11:44,916 --> 00:11:49,156
Speaker 1: a different institution, and usually when you're doing a study

181
00:11:49,236 --> 00:11:52,516
Speaker 1: like that, you want to use patients from the same

182
00:11:52,636 --> 00:11:55,796
Speaker 1: cohort of patients, so the same patient pool that you're

183
00:11:55,796 --> 00:11:59,676
Speaker 1: treating people in. They effectively from what I can tell,

184
00:12:00,796 --> 00:12:04,956
Speaker 1: selected patients, just other patients to compare it to, and

185
00:12:05,076 --> 00:12:09,996
Speaker 1: that's not really a great comparison. Also, you pointed out

186
00:12:10,036 --> 00:12:13,196
Speaker 1: it was very small, So when you're talking about six

187
00:12:13,276 --> 00:12:16,156
Speaker 1: patients out of a group of I think there were

188
00:12:16,196 --> 00:12:21,076
Speaker 1: twenty in the total treatment group, that is not sufficiently

189
00:12:21,236 --> 00:12:24,956
Speaker 1: powered to make any conclusions at all. Those patients could

190
00:12:24,996 --> 00:12:27,876
Speaker 1: have cleared the virus on their own, they could have

191
00:12:27,956 --> 00:12:31,636
Speaker 1: gotten better, we just can't say because not enough patients

192
00:12:31,756 --> 00:12:36,676
Speaker 1: were investigated. There are some other issues as well. One

193
00:12:36,756 --> 00:12:40,796
Speaker 1: issue is that the journal that that paper was originally

194
00:12:40,836 --> 00:12:44,436
Speaker 1: published in after a very short time of being a preprint,

195
00:12:45,396 --> 00:12:48,876
Speaker 1: is controlled. The editor in chief of that journal is

196
00:12:48,996 --> 00:12:52,316
Speaker 1: the senior author of the paper. Did he a Raoul?

197
00:12:52,916 --> 00:12:59,316
Speaker 1: And furthermore, Elizabeth Bake from microbiome Digest has pointed out

198
00:12:59,556 --> 00:13:03,956
Speaker 1: that there are some issues with his publication record. There

199
00:13:03,996 --> 00:13:09,076
Speaker 1: are some papers that he has published with questionable data,

200
00:13:08,796 --> 00:13:13,196
Speaker 1: and not necessarily that that indicates that he's falsifying data

201
00:13:13,436 --> 00:13:17,716
Speaker 1: or or engaging in any intentionally nefarious work. But some

202
00:13:17,836 --> 00:13:21,756
Speaker 1: questions have been raised about the research integrity in general

203
00:13:21,796 --> 00:13:26,276
Speaker 1: of papers that he has authored, and just the conflict

204
00:13:26,316 --> 00:13:29,236
Speaker 1: of interest with you know, him being the editor in

205
00:13:29,316 --> 00:13:31,756
Speaker 1: chief of the journal that this was rushed to publication

206
00:13:31,836 --> 00:13:35,636
Speaker 1: in does not do a lot to support the notion

207
00:13:35,756 --> 00:13:40,876
Speaker 1: that the trial was rigorously evaluated by a panel of

208
00:13:42,316 --> 00:13:46,396
Speaker 1: non conflicted peers. So there are a lot of questions

209
00:13:46,436 --> 00:13:51,036
Speaker 1: about that particular investigator and the studies coming out of

210
00:13:51,076 --> 00:13:54,156
Speaker 1: his group, as well as some of the claims that

211
00:13:54,316 --> 00:13:57,636
Speaker 1: he has made on social media and in the media,

212
00:13:57,916 --> 00:14:01,236
Speaker 1: which is you know, has sort of fueled this their

213
00:14:01,396 --> 00:14:05,196
Speaker 1: miracle drugs and they're going to solve everything. There are

214
00:14:05,236 --> 00:14:09,556
Speaker 1: both scientific as well as really ethical reason That's why

215
00:14:10,276 --> 00:14:13,876
Speaker 1: that work may be somewhat problematic. So we've now evaluated

216
00:14:13,956 --> 00:14:16,436
Speaker 1: that study and you've called it significantly into question and

217
00:14:16,596 --> 00:14:18,196
Speaker 1: very helpful ways. There's a lot there that I had

218
00:14:18,196 --> 00:14:19,876
Speaker 1: not known before and that I think is not generally

219
00:14:19,876 --> 00:14:24,116
Speaker 1: available outside of the expert sphere. However, and here to

220
00:14:24,196 --> 00:14:27,756
Speaker 1: the big However, if I were someone who were very

221
00:14:27,796 --> 00:14:31,916
Speaker 1: sick with coronavirus right now, we're pretty sick, significant shortness

222
00:14:31,916 --> 00:14:33,956
Speaker 1: of breath enough to have to be admitted to a hospital,

223
00:14:34,436 --> 00:14:37,996
Speaker 1: and I had to choose among available options, I'm pretty

224
00:14:38,036 --> 00:14:40,956
Speaker 1: sure that I would ask for this treatment combination. Not

225
00:14:41,076 --> 00:14:42,876
Speaker 1: because I would be convinced of the rigor of the

226
00:14:42,876 --> 00:14:47,916
Speaker 1: study just because there isn't any really other option out there.

227
00:14:48,396 --> 00:14:51,516
Speaker 1: And indeed, you know, just anecdotally, someone who might know

228
00:14:51,596 --> 00:14:53,156
Speaker 1: who was in the hospital, was really sick, was on

229
00:14:53,236 --> 00:14:56,716
Speaker 1: eventilator in New York, was actually given this treatment. And

230
00:14:56,756 --> 00:14:59,996
Speaker 1: I thought to myself, good, So, I guess what I'm

231
00:14:59,996 --> 00:15:01,956
Speaker 1: wondering is about a kind of paradox, right. I mean,

232
00:15:02,036 --> 00:15:05,796
Speaker 1: here you are saying the science is bad and inadequate,

233
00:15:06,236 --> 00:15:10,196
Speaker 1: and yet it still might be better evidence than anything

234
00:15:10,196 --> 00:15:12,076
Speaker 1: else we have, and therefore a reason to give it

235
00:15:12,076 --> 00:15:13,876
Speaker 1: a try in an individual case. Now, I don't think

236
00:15:13,916 --> 00:15:16,076
Speaker 1: I'm crazy. I mean, is that what you would do

237
00:15:16,156 --> 00:15:18,676
Speaker 1: if you were suddenly hospitalized or someone close to you

238
00:15:18,716 --> 00:15:22,036
Speaker 1: were suddenly hospitalized with a serious case. I mean, I

239
00:15:22,076 --> 00:15:24,716
Speaker 1: think that for that reason this has been used in

240
00:15:24,756 --> 00:15:28,796
Speaker 1: those circumstances that would be an appropriate circumstance in which

241
00:15:28,836 --> 00:15:32,036
Speaker 1: to use this. But with the caveat that that is

242
00:15:32,116 --> 00:15:36,916
Speaker 1: definitely a decision for the physician treating a particular patient

243
00:15:37,036 --> 00:15:41,476
Speaker 1: to make. There may be counter indications for taking either

244
00:15:41,516 --> 00:15:45,316
Speaker 1: of those medications. I have read that there are potentially

245
00:15:45,436 --> 00:15:51,636
Speaker 1: drug interactions that can occur between zithromycin and hydroxy chloroquin specifically.

246
00:15:52,316 --> 00:15:56,636
Speaker 1: I'm not sure personally why for the rationale of including

247
00:15:56,676 --> 00:16:00,716
Speaker 1: a zithromycin other than it's an antibiotic that could potentially

248
00:16:00,716 --> 00:16:05,156
Speaker 1: treat secondary bacterial infections, which are probably playing a big

249
00:16:05,276 --> 00:16:09,436
Speaker 1: role in the most severe patients. So it's possible bole

250
00:16:09,516 --> 00:16:13,636
Speaker 1: that a different drug besides zithromycin for somebody, for example,

251
00:16:13,676 --> 00:16:16,836
Speaker 1: who might be allergic to a zithromycin, or have a

252
00:16:16,876 --> 00:16:20,716
Speaker 1: bad reaction to a zithromycin, or have a drug interaction problem,

253
00:16:21,076 --> 00:16:23,996
Speaker 1: they could be given potentially a different antibiotic, and those

254
00:16:24,276 --> 00:16:26,836
Speaker 1: would all be decisions that would be made by the

255
00:16:26,876 --> 00:16:31,316
Speaker 1: physician treating the patient in each individual circumstance. I agree

256
00:16:31,356 --> 00:16:33,956
Speaker 1: with you that for patients who are on a ventilator

257
00:16:33,996 --> 00:16:37,036
Speaker 1: where there is no other option, a physician should be

258
00:16:37,156 --> 00:16:40,436
Speaker 1: able to make a decision about whether to treat that

259
00:16:40,556 --> 00:16:46,156
Speaker 1: patient with an unproven medication that is available and FDA

260
00:16:46,196 --> 00:16:51,076
Speaker 1: approved for other uses. That is a really individual decision

261
00:16:51,156 --> 00:16:54,756
Speaker 1: that needs to be made in terms of patient physician care.

262
00:16:55,316 --> 00:16:58,916
Speaker 1: That's separate from doing a large scale clinical trial to

263
00:16:59,116 --> 00:17:03,516
Speaker 1: determine definitively whether those drugs actually work. You mentioned that

264
00:17:03,636 --> 00:17:07,076
Speaker 1: Zythromycin is a broad spectrum antibiotic. It's not an anti

265
00:17:07,196 --> 00:17:10,396
Speaker 1: viral agent, and so you were speculating that, you know,

266
00:17:10,436 --> 00:17:12,796
Speaker 1: if it's having an effect, maybe the effect that it's

267
00:17:12,796 --> 00:17:16,236
Speaker 1: just having is helping to deal with whatever other bacterial

268
00:17:16,276 --> 00:17:20,756
Speaker 1: infections maybe going on simultaneous to the viral infection. What's

269
00:17:20,756 --> 00:17:25,116
Speaker 1: the mechanism for hydroxy chloroquin that is an antiviral agent? Right,

270
00:17:25,156 --> 00:17:27,636
Speaker 1: So what is the mechanism if you can explain it

271
00:17:27,676 --> 00:17:31,196
Speaker 1: in lay person's terms for us, by which that's supposed

272
00:17:31,236 --> 00:17:33,316
Speaker 1: to have an effect If it is indeed having an effect,

273
00:17:33,876 --> 00:17:38,116
Speaker 1: So that's not known. Hydroxy Chloroquine is actually an anti

274
00:17:38,116 --> 00:17:44,996
Speaker 1: malarial and malaria parasites are not viruses. Actually they're single

275
00:17:45,076 --> 00:17:49,756
Speaker 1: celled parasitic organisms. A group in China looked at the

276
00:17:49,876 --> 00:17:54,356
Speaker 1: effect of chloroquine, which is a related drug in vitro

277
00:17:54,756 --> 00:17:58,956
Speaker 1: on SARS coronavirus and SARS coronavirus to replication and they

278
00:17:59,036 --> 00:18:03,356
Speaker 1: speculate that the block occurs during the entry process. So

279
00:18:03,396 --> 00:18:06,436
Speaker 1: when a virus infects a cell, the virus attaches to

280
00:18:06,676 --> 00:18:09,676
Speaker 1: a host receptor and is taken up inside the cell

281
00:18:10,116 --> 00:18:13,076
Speaker 1: in a compartment called an endosome in order for the

282
00:18:13,156 --> 00:18:16,556
Speaker 1: virus to begin replicating its genome, which is a critical

283
00:18:16,596 --> 00:18:20,876
Speaker 1: step in viral replication, the virus has to escape, essentially

284
00:18:20,956 --> 00:18:26,636
Speaker 1: from that endosomal compartment. That escape process is triggered by

285
00:18:26,996 --> 00:18:31,316
Speaker 1: the acidification of the endosome, so the endosome pH drops

286
00:18:31,716 --> 00:18:35,436
Speaker 1: and that provides a chemical environment in which the virus

287
00:18:35,516 --> 00:18:38,796
Speaker 1: confuse with the endosomal membrane and get inside the cell.

288
00:18:39,436 --> 00:18:44,596
Speaker 1: What hydroxychloroquine and chloroquin do is they prevent endosomal acidification,

289
00:18:44,716 --> 00:18:47,916
Speaker 1: and that has been proposed as a mechanism for how

290
00:18:47,916 --> 00:18:51,236
Speaker 1: it would act as an antiviral drug, So it prevents

291
00:18:51,276 --> 00:18:54,116
Speaker 1: the virus from actually getting into the part of the

292
00:18:54,156 --> 00:18:57,156
Speaker 1: cell that it's going to replicate in by blocking that

293
00:18:57,236 --> 00:19:02,276
Speaker 1: acidification process and keeping it trapped in those endosomes. Another

294
00:19:02,316 --> 00:19:04,796
Speaker 1: one of the potential treatments that's being tested that you

295
00:19:04,836 --> 00:19:09,396
Speaker 1: mentioned is remdesvere, an antiviral drug that I think said

296
00:19:09,636 --> 00:19:13,596
Speaker 1: did not work against ebola, but did have some effects

297
00:19:13,676 --> 00:19:17,756
Speaker 1: against mers. Tell us a little bit about this drug

298
00:19:17,876 --> 00:19:20,356
Speaker 1: and why it's thought that it might actually be effective

299
00:19:20,756 --> 00:19:26,476
Speaker 1: against this coronavirus. So, remdesiviere is a broad spectrum anti

300
00:19:26,596 --> 00:19:30,676
Speaker 1: viral drug that's in a class of drugs called nucleoside analogs,

301
00:19:30,716 --> 00:19:37,756
Speaker 1: and they are a chemical mimic of the ATCG molecules

302
00:19:37,836 --> 00:19:42,276
Speaker 1: that make up DNA or RNA. Technically an RNA it's

303
00:19:42,356 --> 00:19:45,956
Speaker 1: you instead of T, but they're called nucleoside bases. And

304
00:19:46,076 --> 00:19:48,796
Speaker 1: most people are familiar with, you know, the genetic code

305
00:19:49,556 --> 00:19:55,436
Speaker 1: which is made up of at season gs and the enzymine, cytosine,

306
00:19:55,476 --> 00:19:59,916
Speaker 1: and guanidine, and these When the virus genome is replicating,

307
00:20:00,476 --> 00:20:04,516
Speaker 1: these are put by an enzyme called, in the case

308
00:20:04,516 --> 00:20:08,756
Speaker 1: of viruses, an RNA polymerase. They are put into a

309
00:20:08,836 --> 00:20:12,796
Speaker 1: chain and that makes the new genetic material. What these

310
00:20:12,876 --> 00:20:17,796
Speaker 1: nucleoside analogs do is they get inserted into this chain

311
00:20:18,036 --> 00:20:22,316
Speaker 1: instead of the atcorg that is actually supposed to be there,

312
00:20:22,836 --> 00:20:27,676
Speaker 1: and that can cause the genome to be catastrophically mutated

313
00:20:27,916 --> 00:20:33,756
Speaker 1: effectively with these non functional base analogs. It's also been

314
00:20:33,796 --> 00:20:39,116
Speaker 1: proposed that these nucleoside analog drugs can also activate certain

315
00:20:39,516 --> 00:20:44,476
Speaker 1: innate antiviral signaling pathways, and they can also interfere with

316
00:20:44,516 --> 00:20:48,356
Speaker 1: the activity of the polymerase enzyme that is making the

317
00:20:48,476 --> 00:20:51,956
Speaker 1: new copies of RNA. In the case of a coronavirus.

318
00:20:52,676 --> 00:21:00,036
Speaker 1: So these drugs did show promise in preclinical studies against ebola,

319
00:21:00,596 --> 00:21:02,996
Speaker 1: but then it turned out not to work in actually

320
00:21:03,076 --> 00:21:06,916
Speaker 1: Bola patients. There might be some reasons for that that

321
00:21:07,036 --> 00:21:10,036
Speaker 1: don't actually have to do necessarily with the mechanism of

322
00:21:10,036 --> 00:21:13,956
Speaker 1: the drug. One thing about ebola patients is that they

323
00:21:14,076 --> 00:21:18,356
Speaker 1: aren't necessarily coming into an ebola treatment unit or ETU

324
00:21:19,116 --> 00:21:23,356
Speaker 1: when they are early on an infection. Oftentimes, when an

325
00:21:23,356 --> 00:21:26,436
Speaker 1: ebola patient is symptomatic, they will show up at the

326
00:21:26,476 --> 00:21:31,796
Speaker 1: ETU after they're already very sick. And I study ebola,

327
00:21:31,836 --> 00:21:34,916
Speaker 1: I study the host response to ebola in my animal

328
00:21:34,996 --> 00:21:38,436
Speaker 1: models that I study. Once those animals have sort of

329
00:21:38,436 --> 00:21:41,436
Speaker 1: reached a point of no return in terms of their

330
00:21:41,436 --> 00:21:46,236
Speaker 1: host response just being completely screwed up by systemic ebola infection,

331
00:21:46,836 --> 00:21:50,476
Speaker 1: then targeting the virus's ability to replicate may not be

332
00:21:50,636 --> 00:21:54,596
Speaker 1: very helpful. And I wonder if that is why in

333
00:21:54,836 --> 00:21:58,636
Speaker 1: patient trials, why remdzevir was not as effective as it

334
00:21:58,716 --> 00:22:03,076
Speaker 1: appeared to be in preclinical trials, Because often in preclinical trials,

335
00:22:03,116 --> 00:22:06,436
Speaker 1: when you're working with an animal model, you know exactly

336
00:22:06,436 --> 00:22:09,556
Speaker 1: how much that animal was infected with and at what

337
00:22:09,676 --> 00:22:13,996
Speaker 1: time that animal was infected. People don't necessarily know when

338
00:22:14,036 --> 00:22:19,596
Speaker 1: they got infected, so remdesivir's ability to treat ebola patients

339
00:22:19,636 --> 00:22:22,876
Speaker 1: will have a lot to do with at what point

340
00:22:22,956 --> 00:22:27,116
Speaker 1: in the infection you can treat them. For COVID, we

341
00:22:27,236 --> 00:22:31,596
Speaker 1: know that remdesivir has some efficacy against SARS coronavirus two

342
00:22:31,876 --> 00:22:35,076
Speaker 1: in vitro in cultured cells, and we know that it

343
00:22:35,076 --> 00:22:38,036
Speaker 1: seems effective in non human primates that were infected with

344
00:22:38,156 --> 00:22:42,396
Speaker 1: Mer's coronavirus, which is another related coronavirus that causes a

345
00:22:42,476 --> 00:22:46,876
Speaker 1: similar type of disease. Whither it will work in patients,

346
00:22:46,956 --> 00:22:50,876
Speaker 1: I think will be dependent on when those patients are diagnosed.

347
00:22:51,436 --> 00:22:55,756
Speaker 1: So remdesivir treatment as a last resort for patients who

348
00:22:55,796 --> 00:22:59,236
Speaker 1: are already severely ill may or may not have an effect.

349
00:22:59,716 --> 00:23:03,756
Speaker 1: If remdesivir does work by triggering some of these anti

350
00:23:03,836 --> 00:23:06,876
Speaker 1: viral immune responses, it's possible that some of those responses

351
00:23:06,876 --> 00:23:09,916
Speaker 1: may be protective. We just don't know until we are

352
00:23:09,956 --> 00:23:12,116
Speaker 1: able to test this. But that is why it's so

353
00:23:12,196 --> 00:23:16,916
Speaker 1: important to test these treatments on people at different stages

354
00:23:16,956 --> 00:23:20,356
Speaker 1: of the disease and with different clinical manifestations of the disease,

355
00:23:20,436 --> 00:23:24,316
Speaker 1: because we really need to know if, for example, remdesivir

356
00:23:24,356 --> 00:23:28,316
Speaker 1: does appear to work, if treatment begins very early, then

357
00:23:28,356 --> 00:23:30,716
Speaker 1: we need to know that so that patients can begin

358
00:23:30,836 --> 00:23:34,396
Speaker 1: treatment initially when they are diagnosed, rather than waiting for

359
00:23:34,436 --> 00:23:37,636
Speaker 1: them to progress to severe disease, for example. So those

360
00:23:37,636 --> 00:23:41,036
Speaker 1: have a lot of implications for the types of decisions

361
00:23:41,116 --> 00:23:45,196
Speaker 1: that physicians and clinicians will make if that drug does

362
00:23:45,236 --> 00:23:48,596
Speaker 1: prove to be effective. Just a quick mention of the

363
00:23:48,956 --> 00:23:52,476
Speaker 1: HIV protease inhibitors which in one patient seem to maybe

364
00:23:52,516 --> 00:23:55,836
Speaker 1: anecdotally have an effect, what would the theory of the

365
00:23:55,876 --> 00:23:59,476
Speaker 1: mechanism be there, and what's your virological instinct about the

366
00:23:59,556 --> 00:24:03,596
Speaker 1: probabilities of that approach panning out. So I've seen a

367
00:24:03,636 --> 00:24:08,916
Speaker 1: couple preprints that have suggested using in silico, meaning in

368
00:24:09,356 --> 00:24:15,156
Speaker 1: computers analysis alone, showing that there may be some interaction

369
00:24:15,316 --> 00:24:20,156
Speaker 1: with the M one protease of coronaviruses. I'm not clear

370
00:24:20,276 --> 00:24:23,916
Speaker 1: if that is the mechanism. I haven't personally seen data

371
00:24:23,996 --> 00:24:28,556
Speaker 1: to suggest that those protease inhibitors that normally target the

372
00:24:28,676 --> 00:24:34,636
Speaker 1: HIV proteases also would have an impact on the coronavirus protease.

373
00:24:35,316 --> 00:24:40,836
Speaker 1: It's certainly possible many proteases have conserved structural features in

374
00:24:40,956 --> 00:24:44,796
Speaker 1: terms of how they work. I haven't seen any data though,

375
00:24:44,836 --> 00:24:48,996
Speaker 1: that conclusively demonstrates the mechanism by which those HIV protease

376
00:24:49,036 --> 00:24:52,476
Speaker 1: inhibitors would be functional. And I'm really grateful to you

377
00:24:52,676 --> 00:24:54,556
Speaker 1: for your time. Before I let you go, I just

378
00:24:54,596 --> 00:24:56,876
Speaker 1: want to ask, is there something I'm not asking you

379
00:24:56,916 --> 00:24:59,436
Speaker 1: that I should be asking you with respect to the

380
00:24:59,436 --> 00:25:01,636
Speaker 1: treatments that are out there. Is there some important point

381
00:25:01,676 --> 00:25:03,876
Speaker 1: that you think we need to hear that I haven't

382
00:25:03,996 --> 00:25:07,596
Speaker 1: directed you towards. I think the only important point that

383
00:25:07,676 --> 00:25:11,636
Speaker 1: I like to get across is that the trials that

384
00:25:11,676 --> 00:25:14,916
Speaker 1: are proceeding now are going as fast as they can.

385
00:25:15,596 --> 00:25:19,836
Speaker 1: But it really is critical to show efficacy. And another

386
00:25:19,916 --> 00:25:23,956
Speaker 1: great example of this is Ebola. So during the West

387
00:25:23,996 --> 00:25:27,996
Speaker 1: African Ebola outbreak, a number of patients that were evacuated

388
00:25:28,116 --> 00:25:31,316
Speaker 1: from West Africa were then treated with an experimental drug

389
00:25:31,556 --> 00:25:35,676
Speaker 1: called ZMP that everybody heard about, and many of those

390
00:25:35,756 --> 00:25:40,756
Speaker 1: patients recovered and people attributed to that to look at

391
00:25:40,836 --> 00:25:43,916
Speaker 1: wonderful z MAP. It works so well. Z MAP failed

392
00:25:43,956 --> 00:25:47,156
Speaker 1: the same clinical trial that m Dozevier did. It now

393
00:25:47,156 --> 00:25:51,196
Speaker 1: appears quite clear that getting just supportive care, so fluids,

394
00:25:52,036 --> 00:25:57,356
Speaker 1: potentially breathing support, other types of treatments for the symptoms

395
00:25:57,396 --> 00:26:01,196
Speaker 1: of Ebola disease. That type of supportive hospital care has

396
00:26:01,276 --> 00:26:05,316
Speaker 1: been itself effective at really improving the case fatality rates

397
00:26:05,356 --> 00:26:08,916
Speaker 1: for ebola. So it's possible that all those patients that

398
00:26:08,956 --> 00:26:10,996
Speaker 1: go z MAP, who were all you know, in the

399
00:26:11,116 --> 00:26:14,596
Speaker 1: United States for the most part, or Europe in state

400
00:26:14,596 --> 00:26:18,516
Speaker 1: of the art, I see us getting world class supportive

401
00:26:18,596 --> 00:26:21,276
Speaker 1: care that may have had more of an impact than

402
00:26:21,436 --> 00:26:24,476
Speaker 1: z MAP, But because it was a handful of patients

403
00:26:24,476 --> 00:26:28,316
Speaker 1: with no control group, we couldn't evaluate z MAP, so people,

404
00:26:28,476 --> 00:26:30,796
Speaker 1: I think sort of jumped to the conclusion that it

405
00:26:30,836 --> 00:26:32,916
Speaker 1: was z MAP that was doing this and not the

406
00:26:33,036 --> 00:26:36,556
Speaker 1: other different types of care that those patients were receiving.

407
00:26:36,956 --> 00:26:39,556
Speaker 1: So we just need to be really careful about attributing

408
00:26:40,116 --> 00:26:44,116
Speaker 1: positive outcomes to the wrong thing. Lest you know, people

409
00:26:44,156 --> 00:26:47,276
Speaker 1: start prescribing these drugs widely. They don't do anything. It

410
00:26:47,316 --> 00:26:49,636
Speaker 1: gives people a false sense of security, and it could

411
00:26:49,716 --> 00:26:53,836
Speaker 1: ultimately be more harmful to public health and helpful. Angela,

412
00:26:53,876 --> 00:26:56,436
Speaker 1: thank you so much for your time and your expertise

413
00:26:56,476 --> 00:27:00,676
Speaker 1: and your extremely clear headed analysis and your excellent way

414
00:27:00,716 --> 00:27:04,116
Speaker 1: of making it all understandable even to a layman like me.

415
00:27:04,196 --> 00:27:06,156
Speaker 1: Thank you so much for your time. Well, it's my pleasure.

416
00:27:06,236 --> 00:27:07,836
Speaker 1: Thank you for having me on and giving me the

417
00:27:07,876 --> 00:27:11,756
Speaker 1: opportunity to talk to your listeners about this. I learned

418
00:27:11,796 --> 00:27:15,476
Speaker 1: a lot from talking to doctor Angela russ Mussen. In particular,

419
00:27:15,596 --> 00:27:19,676
Speaker 1: she was extremely clear about the necessity of patience and

420
00:27:19,796 --> 00:27:23,676
Speaker 1: good scientific technique in trying to figure out what treatment

421
00:27:23,836 --> 00:27:27,036
Speaker 1: actually will respond to the novel coronavirus in a way

422
00:27:27,036 --> 00:27:30,676
Speaker 1: that works. Like a lot of people, I'm eager for

423
00:27:30,756 --> 00:27:33,636
Speaker 1: there to be a treatment that works right away. And

424
00:27:33,716 --> 00:27:36,916
Speaker 1: you might have heard in my voice some wish, some

425
00:27:37,116 --> 00:27:41,116
Speaker 1: fantasy that we could sidestep some of the most slow

426
00:27:41,236 --> 00:27:45,196
Speaker 1: moving and precise scientific features of experimentation in order to

427
00:27:45,196 --> 00:27:49,156
Speaker 1: get to a treatment, but Angela made it extremely clear

428
00:27:49,596 --> 00:27:51,836
Speaker 1: that the danger in doing so is that we might

429
00:27:51,956 --> 00:27:55,516
Speaker 1: end up mistakenly treating people with drugs that aren't actually

430
00:27:55,516 --> 00:27:58,596
Speaker 1: solving the problem, a phenomenon that she noted did happen

431
00:27:58,636 --> 00:28:02,676
Speaker 1: in some instances in ebola. Response, that means that we

432
00:28:02,796 --> 00:28:06,396
Speaker 1: need to do the slow, careful science in order to

433
00:28:06,436 --> 00:28:09,356
Speaker 1: make sure that people are cured, and in the mean time,

434
00:28:09,716 --> 00:28:13,116
Speaker 1: physicians will keep on using experimental treatments, even if they

435
00:28:13,116 --> 00:28:15,476
Speaker 1: don't know that they work for certain, in the hopes

436
00:28:15,596 --> 00:28:19,516
Speaker 1: that they will have some effect. That combination makes me

437
00:28:19,676 --> 00:28:22,356
Speaker 1: a little more hardened. But at the same time, my

438
00:28:22,516 --> 00:28:26,036
Speaker 1: ultimate takeaway from listening to Angela is that randomness is

439
00:28:26,076 --> 00:28:29,556
Speaker 1: a real risk. It is simply possible that we don't

440
00:28:29,596 --> 00:28:33,796
Speaker 1: have immediately to hand any treatment that will effectively address

441
00:28:34,036 --> 00:28:36,996
Speaker 1: the health challenges that we're facing. And if that's so,

442
00:28:37,756 --> 00:28:41,036
Speaker 1: it's social distancing for all of us and for a

443
00:28:41,076 --> 00:28:44,756
Speaker 1: lot longer. If there's progress with respect to any of

444
00:28:44,756 --> 00:28:47,836
Speaker 1: these treatments, you can be sure we'll discuss that issue

445
00:28:47,916 --> 00:28:51,956
Speaker 1: and get behind the story of the science until I

446
00:28:51,956 --> 00:28:55,596
Speaker 1: speak to you next time. Be careful, be safe, be well.

447
00:28:57,516 --> 00:29:00,476
Speaker 1: Deep background is brought to you by Pushkin Industries. Our

448
00:29:00,516 --> 00:29:04,476
Speaker 1: producer is Lydia Jane Cott, with research help from zooe Wynn.

449
00:29:04,956 --> 00:29:08,596
Speaker 1: Mastering is by Jason Gambrell and Martin Gonzalez. Our showrunner

450
00:29:08,636 --> 00:29:11,556
Speaker 1: is soph Given. Our theme music is composed by Luis

451
00:29:11,636 --> 00:29:16,076
Speaker 1: Gera special thanks to the Pushkin Brass Malcolm Gladwell, Jacob Weisberg,

452
00:29:16,116 --> 00:29:19,356
Speaker 1: and Mia Loebell. I'm Noah Feldman. I also write a

453
00:29:19,356 --> 00:29:21,956
Speaker 1: regular column for Bloomberg Opinion, which you can find at

454
00:29:21,996 --> 00:29:26,356
Speaker 1: bloomberg dot com slash Feldman. To discover Bloomberg's original slate

455
00:29:26,356 --> 00:29:30,676
Speaker 1: of podcasts, go to Bloomberg dot com slash Podcasts. You

456
00:29:30,676 --> 00:29:33,876
Speaker 1: can follow me on Twitter at Noah R. Feldman. This

457
00:29:34,236 --> 00:29:35,276
Speaker 1: is deep background